Production treatment methods such as hydraulic fracture stimulation, carbon dioxide injection, gas injection, and steam or water injection are currently being used on an increasing number of oil and gas asset classes to optimize resource extraction. During these recovery efforts subsequent changes in fluid saturation and pressure produce dynamic changes in the physical properties of the reservoir. An example of this is the passive microseismic energy that typically results from shear stress release on pre existing faults and fractures due to production or injection induced perturbations to the effective stress conditions. These stress changes may be due to reservoir depletion, water flooding or stimulation (i.e. hydraulic fracturing) operations. Accordingly, passive microseismic imaging has become widely employed during these recovery efforts, for example, using a hydraulic fracture and monitoring system.
Such a hydraulic fracture and monitoring system is preferably arranged with respect to at least first and second wellbores. The first wellbore traverses a formation with a zone that is scheduled for hydraulic fracture. A hydraulic fracture apparatus, for example, comprising a fracture fluid, a pump, and controls is coupled to the first wellbore. The second wellbore contains one or more, and preferably a plurality, of temporary or permanent seismic sensors (e.g. geophones). Alternatively, the sensors may be placed along a surface or possibly within the first wellbore. A communication cable such as a telemetry wire facilitates communication between the sensors and a computer data acquisition and control system. As a fracture job commences, fracture fluid is pumped into the first wellbore causing perturbations to the effective stress conditions, creating microseismic events as shear stress is released on pre-existing faults and fractures. The microseismic events create seismic waves that are received by the sensors.
The seismic signals received by the sensors may be used to monitor and map microseismic events caused by the fracture operation. Accordingly, based on the seismic signals received, computers, such as the computer data acquisition and control system, may run programs containing instructions, that, when executed, perform methods for monitoring, detecting, and locating microseismic events. An operator may receive results of the methods in real time as they are displayed on a monitor. The operator may, in turn, adjust hydraulic fracture parameters such as pumping pressure, stimulation fluid, and proppant concentrations to optimize wellbore stimulation based on the displayed information relating to detected and located seismic events.
Event source localization resides at the core of most microseismic monitoring and analysis techniques, where the spatial distribution and time evolution of estimated event locations are often used to infer regions of activity in a reservoir and to attempt to quantify the associated spatio-temporal changes to several important properties such as local stress fields, permeability matrices, fracture fairway connectivity, and the geometric extent of fracture growth. Due to background noise and modeling uncertainties, source location estimates will fluctuate statistically and exhibit variances about true source locations. Because interpretations based on these estimated event locations can have a large impact on resource management decisions, knowledge of the accuracy of such location estimates is of high importance.
The accuracy of microseismic location estimates is a topic of current research, and is a problem that has been approached through several methodologies including computational simulation, analysis of field measurements, and the application of analytic techniques. See, for example, Leo Eisner, Peter M. Duncan, Werner M. Heigl, and William R. Keller, “Uncertainties in passive seismic monitoring,” The Leading Edge, June, 2009 (hereinafter “[1]”); James T. Rutledge and W. Scott Phillips, “Hydraulic stimulation of natural fractures as revealed by induced microearthquakes, Carthage Cotton Valley gas field, east Texas,” Geophysics, Vol. 68, No. 2, 2003 (hereinafter “[2]”); Leo Eisner, Tomas Fischer, and James T. Rutledge, “Determination of S-wave slowness from a linear array of borehole receivers,” Geophysics, 176, 31-39 (hereinafter “[3]”); Robert Kidney, Ulrich Zimmer and Neda Boroumand, “Impact of distance-dependent location dispersion error on interpretation of microseismic event distributions,” The Leading Edge, March, 2010 (hereinafter “[4]”); Vladimir Grenchka, “Data-acquisition design for microseismic monitoring,” The Leading Edge, March, 2010 (hereinafter “[5]”); Leo Eisner, B. J. Hulsey, Peter Duncan, Dana Jurick, Heigl Werner, William Keller, “Comparison of surface and borehole locations of induced seismicity,” Geophysical Prospecting, 2010 (hereinafter “[6]”); Shawn Maxwell, Microseismic location uncertainty. CSEG Recorder, pp. 41-46, April, 2009 (hereinafter “[7]”); and S. C. Maxwell, B. Underhill, L. Bennett, C. Woerpel and A. Martinez, Key criteria for a successful microseismic project. SPE paper 134695, September 2010 (hereinafter “[8]”).
In general, these previous studies have each tended to address location estimation accuracy through examination of one aspect or portion of the typical location estimation process such as optimal placement of a geophone array for microseismic monitoring, estimation of parameters describing observed signals such as times of arrival and particle polarizations, and how variance in the estimation of such parameters translates to uncertainty in resulting source location estimates.
Meanwhile, certain other approaches relating to microseismic event location analysis have been attempted. For example, WO2002/085848 and WO2011/077223 both describe statistical analyses of location estimates. In WO2002/085848, a collection of a large number of events are analyzed, and an estimate is performed to consider events that may not have been detected to update the statistics of the events. In WO2011/077223, events are plotted in 3D, and the bounds of various clusters of events are determined using statistics.
In other approaches, WO2004/070424 describes an approach to estimating times of arrival and includes an ad hoc technique for estimating uncertainty using a covariance of residuals. WO2010/116236 observes that P and S waves have different frequency content, but only uses this observation in an ad hoc way. And finally, WO2008/124759 describes conventional seismic analysis techniques that derive error ellipses around earthquake events. However, only a single result is provided, and there is no comprehensive method or framework for including additional parameters such as range, angle, sensor-source geometry, noise environment, etc.
A need remains, therefore, for a more robust and comprehensive framework for analytically studying microseismic source location estimation accuracy.